arXiv:2606.18663v1 Announce Type: new Abstract: Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale proxy runs. We propose RegMix-D, a simple extension of RegMix to dynamic mixing. Our key observation is that proxy runs produce not only endpoint losses, but also full loss trajectories, which can be used to further improve data mixture. By training regression model on these trajectories, we can predict optimal mixtures at multiple training stages. RegMix-D supports tw
The continuous drive for more efficient and performant Large Language Models (LLMs) necessitates advanced data mixture selection techniques, leading to innovations like dynamic mixing approaches.
Improved data mixing techniques like RegMix-D can significantly boost the efficiency and performance of LLM pretraining, directly impacting the development pace and capabilities of AI systems.
The shift from static to dynamic data mixture selection for LLMs introduces a more adaptive and potentially more effective pretraining methodology, allowing models to learn better from available data at different stages.
- · AI researchers
- · LLM developers
- · Cloud providers offering AI compute
- · Companies utilizing advanced LLMs
- · Less efficient LLM pretraining methods
- · Organizations without access to advanced AI research
RegMix-D allows for more optimized data feeding during LLM training, potentially leading to faster training times and improved model accuracy.
More efficient LLMs could reduce the computational resources needed for pretraining, making advanced AI development more accessible and cost-effective.
The acceleration of LLM development could lead to a faster deployment of more sophisticated AI agents and applications across various industries.
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